Fast Context Adaptation via Meta-Learning

We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation. We show empirically that CAVIA outperforms MAML for regression, classification, and reinforcement learning. Our experiments also highlight weaknesses in current benchmarks, in that the amount of adaptation needed in some cases is small.Comment: Published at the International Conference on Machine Learning (ICML) 201

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains

Atom in a coherently controlled squeezed vacuum

A broadband squeezed vacuum photon field is characterized by a complex squeezing function. We show that by controlling the wavelength dependence of its phase it is possible to change the dynamics of the atomic polarization interacting with the squeezed vacuum. Such a phase modulation effectively produces a finite range temporal interaction kernel between the two quadratures of the atomic polarization yielding the change in the decay rates as well as the appearance of additional oscillation frequencies. We show that decay rates slower than the spontaneous decay rate can be achieved even for a squeezed bath in the classic regime. For linear and quadratic phase modulations the power spectrum of the scattered light exhibits narrowing of the central peak due to the modified decay rates. For strong phase modulations side lobes appear symmetrically around the central peak reflecting additional oscillation frequencies.Comment: 4 pages, 4 figure

Impact of Pedometer Use and Self-Regulation Strategies on Junior High School Physical Education Students\u27 Daily Step Counts

Background: The aim of this study was to determine the impact of pedometer use and self-regulation strategies on adolescents’ daily physical activity. Methods: Junior high school students (n = 113) enrolled in seventh- and eighth-grade physical education classes (52 girls, 61 boys) volunteered to participate in a 5-week study to assess daily step counts. Ten physical education classes were randomly assigned to 1 of 3 groups: (a) self-regulation, (b) open, and (c) control. Results: A repeated-measures, mixed-model analysis of variance revealed a significant 3 × 4 (Group by Time) interaction effect, F6,290 = 2.64, P \u3c .02. Followup analyses indicated participants in the self-regulation group took 2071 to 4141 more steps/d than the control. No other significant differences emerged among groups on step counts. Conclusions: It appears that having access to and charting daily step counts (ie, self-regulatory strategies) positively influenced young adolescents to attain a higher number of steps/d

The ultrafilter number for singular cardinals

We prove the consistency of a singular cardinal $\lambda$ with small value of the ultrafilter number $u_\lambda$, and arbitrarily large value of $2^\lambda$.Comment: 8 page

Using Big Bang Nucleosynthesis to Extend CMB Probes of Neutrino Physics

We present calculations showing that upcoming Cosmic Microwave Background (CMB) experiments will have the power to improve on current constraints on neutrino masses and provide new limits on neutrino degeneracy parameters. The latter could surpass those derived from Big Bang Nucleosynthesis (BBN) and the observationally-inferred primordial helium abundance. These conclusions derive from our Monte Carlo Markov Chain (MCMC) simulations which incorporate a full BBN nuclear reaction network. This provides a self-consistent treatment of the helium abundance, the baryon number, the three individual neutrino degeneracy parameters and other cosmological parameters. Our analysis focuses on the effects of gravitational lensing on CMB constraints on neutrino rest mass and degeneracy parameter. We find for the PLANCK experiment that total (summed) neutrino mass $M_{\nu} > 0.29$ eV could be ruled out at $2\sigma$ or better. Likewise neutrino degeneracy parameters $\xi_{\nu_{e}} > 0.11$ and $| \xi_{\nu_{\mu/\tau}} | > 0.49$ could be detected or ruled out at $2\sigma$ confidence, or better. For POLARBEAR we find that the corresponding detectable values are $M_\nu > 0.75 {\rm eV}$, $\xi_{\nu_{e}} > 0.62$, and $| \xi_{\nu_{\mu/\tau}}| > 1.1$, while for EPIC we obtain $M_\nu > 0.20 {\rm eV}$, $\xi_{\nu_{e}} > 0.045$, and $|\xi_{\nu_{\mu/\tau}}| > 0.29$. Our forcast for EPIC demonstrates that CMB observations have the potential to set constraints on neutrino degeneracy parameters which are better than BBN-derived limits and an order of magnitude better than current WMAP-derived limits.Comment: 27 pages, 11 figures, matches published version in JCA

Computing Convex Coverage Sets for Faster Multi-objective Coordination

In this article, we propose new algorithms for multi-objective coordination graphs (MO- CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set (CCS) instead of a Pareto coverage set (PCS). Not only is a CCS a sufficient solution set for a large class of problems, it also has important characteristics that facilitate more efficient solutions. We propose two main algorithms for computing a CCS in MO-CoGs. Convex multi-objective variable elimination (CMOVE) computes a CCS by performing a series of agent eliminations, which can be seen as solving a series of local multi-objective subproblems. Variable elimination linear support (VELS) iteratively identifies the single weight vector w that can lead to the maximal possible improvement on a partial CCS and calls variable elimination to solve a scalarized instance of the problem for w. VELS is faster than CMOVE for small and medium numbers of objectives and can compute an ε-approximate CCS in a fraction of the runtime. In addition, we propose variants of these methods that employ AND/OR tree search instead of variable elimination to achieve memory efficiency. We analyze the runtime and space complexities of these methods, prove their correctness, and compare them empirically against a naive baseline and an existing PCS method, both in terms of memory-usage and runtime. Our results show that, by focusing on the CCS, these methods achieve much better scalability in the number of agents than the current state of the art